"""
Single-objective optimization example using pymoo.
This script demonstrates basic single-objective optimization
using the Genetic Algorithm on the Sphere function.
"""
from pymoo.algorithms.soo.nonconvex.ga import GA
from pymoo.problems import get_problem
from pymoo.optimize import minimize
from pymoo.operators.crossover.sbx import SBX
from pymoo.operators.mutation.pm import PM
from pymoo.operators.sampling.rnd import FloatRandomSampling
from pymoo.termination import get_termination
import numpy as np
def run_single_objective_optimization():
"""Run single-objective optimization example."""
# Define the problem - Sphere function (sum of squares)
problem = get_problem("sphere", n_var=10)
# Configure the algorithm
algorithm = GA(
pop_size=100,
sampling=FloatRandomSampling(),
crossover=SBX(prob=0.9, eta=15),
mutation=PM(eta=20),
eliminate_duplicates=True
)
# Define termination criteria
termination = get_termination("n_gen", 100)
# Run optimization
result = minimize(
problem,
algorithm,
termination,
seed=1,
verbose=True
)
# Print results
print("\n" + "="*60)
print("OPTIMIZATION RESULTS")
print("="*60)
print(f"Best solution: {result.X}")
print(f"Best objective value: {result.F[0]:.6f}")
print(f"Number of generations: {result.algorithm.n_gen}")
print(f"Number of function evaluations: {result.algorithm.evaluator.n_eval}")
print("="*60)
return result
if __name__ == "__main__":
result = run_single_objective_optimization()